Signal processing and sampling method for obtaining time series corresponding to higher order derivatives.
Simulation of high available fault-tolerant systems by simulating stiff and large Markov chains.
Smoothing and occupation measures of stochastic processes
This is a review paper about some problems of statistical inference for one-parameter stochastic processes, mainly based upon the observation of a convolution of the path with a non-random kernel. Most of the results are known and presented without proofs. The tools are first and second order approximation theorems of the occupation measure of the path, by means of functionals defined on the smoothed paths. Various classes of stochastic processes are considered starting with the Wiener process,...
Smoothing dichotomy in randomized fixed-design regression with strongly dependent errors based on a moving average
We consider a fixed-design regression model with errors which form a Borel measurable function of a long-range dependent moving average process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We show that the Priestley-Chao kernel estimator of the regression fuction exhibits a dichotomous asymptotic behaviour depending on the amount of smoothing employed. Moreover, the resulting estimator is shown to exhibit...
Smoothness of Metropolis-Hastings algorithm and application to entropy estimation
The transition kernel of the well-known Metropolis-Hastings (MH) algorithm has a point mass at the chain’s current position, which prevent direct smoothness properties to be derived for the successive densities of marginals issued from this algorithm. We show here that under mild smoothness assumption on the MH algorithm “input” densities (the initial, proposal and target distributions), propagation of a Lipschitz condition for the iterative densities can be proved. This allows us to build a consistent...
Sobre la interpretación de modelos ARIMA univariantes.
En este trabajo se demuestra cómo describir un modelo ARIMA de series temporales como suma de una tendencia a largo plazo, un componente estacional y un componente transitorio. Esta descomposición se obtiene a partir de la función de predicción del modelo, y su uso permite apreciar aspectos poco estudiados de los modelos ARIMA.
Sobre la robustificación interna del algoritmo de Plackett-Kalman para la estimación recursiva del modelo de regresión lineal.
Este trabajo presenta un procedimiento para hacer robusto el algoritmo recursivo de Plackett-Kalman para el modelo lineal, incorporándole medidas diagnósticas que indiquen la influencia potencial y real de cada nueva observación en los parámetros del modelo. Se describe cómo calcular recursivamente el estadístico D2 de Cook, la distancia de Mahalanobis de cada nueva observación al centro de gravedad de la ya incluidas, y un contraste, basado en los residuos recursivos, de que la nueva observación...
Some applications of time series models to financial data
Some generalizations in a heteroscedastic RCA (1) model
Some investigations in minimax estimation theory [Book]
Some limit properties of an approximate least squares estimator in a nonlinear regression model with correlated nonzero mean errors.
Some models for the voltage.
Some New Time Series Results.
Some nonlinear statistical problems of a Poisson process
Some parameter estimation problems for hypoelliptic homogeneous Gaussian diffusions
Some problems of exponential smoothing
The paper deals with some practical problems connected with the classical exponential smoothing in time series. The fundamental theorem of the exponential smoothing is extended to the case with missing observations and an interpolation procedure in the framework of the exponential smoothing is described. A simple method of the exponential smoothing for multivariate time series is suggested.
Some Results About Averaging in Stochastic Approximation.
Some results for the quadratic analysis of Gaussian processes and applications
Some results on exponential families of Markov processes
Some results on the spectral analysis of nonstationary time series.